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IEEE Transactions on Pattern Analysis and Machine Intelligence|January 22, 2026
Wasserstein Distances Made Explainable: Insights into Dataset Shifts and Transport PhenomenaPhilip Naumann, Jacob Kauffmann, Gregoire MontavonIEEE Transactions on Pattern Analysis and Machine Intelligence|April 12, 2024
Disentangled Explanations of Neural Network Predictions by Finding Relevant SubspacesPattarawat Chormai, Jan Herrmann, Klaus-Robert Muller, et al.IEEE Transactions on Neural Networks and Learning Systems|August 31, 2016
Evaluating the Visualization of What a Deep Neural Network Has LearnedWojciech Samek, Alexander Binder, Gregoire Montavon, et al.IEEE Transactions on Neural Networks and Learning Systems|July 7, 2022
From Clustering to Cluster Explanations via Neural NetworksJacob Kauffmann, Malte Esders, Lukas Ruff, et al.IEEE Transactions on Pattern Analysis and Machine Intelligence|September 2, 2020
Building and Interpreting Deep Similarity ModelsOliver Eberle, Jochen Buttner, Florian Krautli, et al.IEEE Transactions on Pattern Analysis and Machine Intelligence|September 24, 2021
Higher-Order Explanations of Graph Neural Networks via Relevant WalksThomas Schnake, Oliver Eberle, Jonas Lederer, et al.Pageof 1